There has been a rapid growth in the availability and retrieval of information through the World Wide Web. The web contains large volume of data that is stored in database data structures such as database tables or indexes or files. The data stored may be of different formats, different types, and may contain different domain-specific information.
The web has gained importance due to its usage in different applications such as e-commerce, hospitality, social networking etc. It enables individuals across the globe to collaborate together. The internet websites contain various files stored on the server receiving requests from different clients for retrieval of data hosted by these websites.
Search engines such as Google™, Yahoo® etc facilitate searching through the large volume of data by indexing the keywords and establishing relevancy thereof with user search criterion. Most of the search engines provide set of results that are based on keywords used by the user in the search queries formulated for searching over the web and are generally presented in the descending order of relevancy.
Most of the users tend to search the information from the web that is relevant to the domain in which they are interested. So, while searching users normally use keywords and variants thereof to obtain relevant set of results. However, information retrieval from large volume of data with different formats, types and context is a problematic and a tedious task.
Moreover, if the file to be searched is media file then it is not possible to search the required media file using keywords media content does not contain associated keywords.
For example, if the user is searching a video or a photo from the website storing videos or photos, then it will be very difficult for the user to search for a particular video or photo in which she is interested due to lack of keywords or metadata associated with the file. Thus, these files need to be tracked and identified in an efficient manner so that they can be retrieved easily.
One method that has gained importance today is online tagging of objects. The tagging helps in object recognition. The tagging is the phenomenon wherein the users connected on internet add tags to the file or movie clip or image stored on the internet so that it can be searched easily by the user interested in searching these stored files on the internet. This is referred as “Social Tagging”.
Further, the social tagging enables storage of tags generated from multiple users on the server and associates these tags to a particular group. This allows social communities to maintain their own sets of tags for the same objects in the same video. This leads to faster and more relevant searches when a user is using the tag to search for objects (and related video, metadata, etc) in a particular social community. For example, the social community interested in Smartphones may generate a social group in context to Smartphones and store the tags generated from the Smartphone community members in a database storing tags generated from the users related to the Smartphones. This facilitates in searching of any particular social community group as well, for example Smartphones group in this case.
Efforts have been made in the past for online tagging of objects. Few of those known to us are as follows:
One such augmented reality application known to us is Google™ Goggles that is able to direct relevant searches and therefore information from an image.
Further, there exist open source projects that deal with object tracking, in particular motion history image. In OpenCv there are a number of tracking methods such as Camshift & Meanshift demo, lkedemo, eye tracking.
Social networking on TV is although a very recent concept, has been gaining traction as a showcase application for Social TV.
Tagging on the other hand has been used with some success to auto-generate metadata information by geo-locating photographs taken by mobile phones.
Manual tagging has greatly improved image search as can be seen with Flickr® and Picasa™ from Yahoo® and Google™ respectively.
Also, there exists APIs in the art such as Future API and Amazon's Mechanical Turk HITs that are being used to tag proprietary videos or surveillance videos for a fee.
Current social TV applications allow users to view what other users connected to them are watching and often use that to create communities and allow users to chat on the same. This level of interaction, however, is limited to text.
Although there has been extensive research on the motion tracking of the objects, attaching meaning to the object tracked and identified is still a hard computational problem. Moreover, tracking of videos itself is a problem due to very low resolution of the videos. Further, the problem with tracking of videos increases due to varying environmental conditions such as illumination, occlusion problems etc.
Also, if the object to be tracked is moving at high speed, it is difficult to tack it. Similarly, the object with low frame rate is difficult to track.
As will be appreciated, there is a clear need for an improved method of processing and metatagging image content such as video content that would simplify the long-standing computationally hard problem of image processing for object identification and recognition, which would further alleviate many of the problems outlined above.